Field of the Invention
The present invention relates to a method of analyzing an image and determining whether to output image data associated with an area of the image. Such image data can be used in further training of the object detection algorithm.
Description of the Related Technology
Object detection algorithms can be used to detect and track objects in images, such as still or live video, for example as captured by an image sensor in a digital camera.
Detecting and tracking objects can be based on classifier models derived through off-line training. In one such approach, a positive and negative dataset is created, consisting of a large number of examples in which the object of interest is respectively present or absent.
A large sample set of the object of interest may be required to allow adequate performance. Also, variations in the conditions of the actual capture environment may reduce accuracy. For example, in the case of face detection, it is common to train a classifier based on examples of human faces viewed at eye level. However, in some applications it is desirable to place a camera at an elevated angle, which reduces the accuracy of face detection, leading to increased false positives and negatives with respect to a camera positioned at eye level.
One approach to overcoming these issues is to manually create a number of training datasets and train a classifier for each set, producing a number of models each optimized to a given environment. However, this requires the generation of additional large sample datasets, which in turn involves significant human intervention in selecting and annotating the dataset elements. Also, in the case of elevated camera positions, it is difficult for a human to select the appropriate positive and negative training sets manually due to the variation in e.g. facial proportions as a function of elevation angle. Improving an existing dataset similarly involves large amounts of human effort.